Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test and the moving t test were used to diagnose the runoff mutation time. Furthermore, the slope changing ratio of cumulative quantity method (SCRCQ), climate elasticity method, and Budyko equation were utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities. The following results were obtained: (1) The Upper Shule River experienced a significant increase in runoff from 1972 to 2021, with 1998 marking the year of abrupt change. (2) The runoff sensitivity showed a downward trend from 1972 to 2021. The main factor affecting the decrease in runoff sensitivity was the characteristic parameters of underlying surface (n), followed by precipitation (P), while the influence of potential evapotranspiration (ET₀) was the weakest. (3) The response of runoff changes to runoff sensitivity and influencing factors were 90.32% and 9.68%, respectively. (4) The results of three attribution methods indicated that climate change was the primary factor causing the alteration of runoff in the Upper Shule River. The research results supplement the hydrological change mechanisms of the Upper Shule River and provide a scientific basis for future water resources management and flood control measures.

Groundwater research on climate change is one of the significant topics in the field of natural sciences that is receiving increasing attention. However, no bibliometric research papers have been published that have analyzed the field for relevant knowledge graphs and relational networks. R-Bibliometrix and VOSviewer software are used to quantitatively analyze and visually represent the literature on the research of groundwater under climate change from 2010 to 2024 based on the Web of Science Core Collection. The findings demonstrated that a total of 4748 articles have been published on this topic, with publications increasing yearly. The most influential journal was the Journal of Hydrology, and the ten most influential papers were published from 2013 to 2017. The United States and China published significantly more research than any other country and had the closest cooperation, while Europe is the continent with the most publications. The Chinese Academy of Sciences was the leading institution in terms of the number of publications, and the Delft University of Technology was the institution with the highest average number of citations. British scholar Chris Soulsby was the author with the highest number of articles. The top three keywords were ‘climate change’, ‘groundwater’, and ‘model’. Machine-learning methods and remote-sensing techniques were emerging research hotspots that guided future research directions.

To explore the mechanisms driving variation in ET₀ (reference evapotranspiration) in an arid inland region of Northwest China, daily meteorological data from 1960 to 2019 from 19 meteorological stations in the Taohe River basin and its surrounding areas were used to analyze the temporal and spatial distributions of ET₀ and meteorological variables. Various qualitative and quantitative analysis methods were used to reveal the correlation between ET₀ and meteorological variables. The degree of sensitivity of ET₀ variations to meteorological variables and the contribution from each meteorological variable were clarified, and the mechanisms driving variation in ET₀ were fully revealed. These are the results: (1) ET₀ in the Taohe River basin presented a significant upward trend with a linear change rate of 0.93 mm/a, and a sudden change occurred in 1994. The spatial variation in ET₀ ranged from 779.8 to 927.6 mm/a, with low values in the upper and middle reaches and high values in the lower reaches. The ET₀ at 14 stations (73.68% of the total) was significantly increased (p < 0.05), and that at 5 stations (26.32% of the total) was not significantly increased (p > 0.05). (2) RH, R_n, and u₂ did not change significantly, while T_max and T_min showed a significant upward trend. (3) R_n is a meteorological variable closely related to variations in ET₀, and is the most sensitive variable for variations in ET₀, followed by T_max and u₂. (4) T_max is the meteorological variable that contributes most to the variation in ET₀ (30.98%), followed by T_min (29.11%), u₂ (6.57%), R_n (2.22%), and RH (0.05%). The research results provide a scientific basis for the rational and efficient utilization of water resources and the maintenance of ecosystem health.